Sql server 2016 with R

Introducing Microsoft SQL Server R Services.
With the release of CTP3 SQL Server 2016 and its native In-database support for the open source R language you can now call both R, RevoScaleR functions and scripts directly from within a SQL query and benefit from multi-threaded and multi-core in-DB computations.

You can use the rich and powerful R language and the many packages from the community to create models and generate predictions using your SQL Server data. Because R Services (In-database) integrates the R language with SQL Server, you can keep analytics close to the data and eliminate the costs and security risks associated with data movement. In this post, we will see how we can leverage SQL Server 2016 as a Scoring Engine to predict “bad” loans. Loans that indicate good repayment behavior are considered “good” and loans that indicate less than perfect repayment behavior are considered “bad”. The Azure Data Science VM comes pre-installed with SQL Server 2016 Developer edition and can be used readily for our scoring experiment. This experiment consists of 6 steps. Create a DB, say ‘lendingclub’ in SQL Server CREATE DATABASE [lendingclub] EXEC [dbo].

Accurate estimation of bike demand at different locations and different times would help bicycle-sharing systems better meet rental demand and allocate bikes to locations. In this blog post, we walk through how to use Microsoft R Server (MRS) to build a regression model to predict bike rental demand. In the example below, we demonstrate an end-to-end machine learning solution development process in MRS, including data importing, data cleaning, feature engineering, parameter sweeping, and model training and evaluation.

Data The Bike Rental UCI dataset is used as the input raw data for this sample. The dataset contains 17,379 rows and 17 columns, with each row representing the number of bike rentals within a specific hour of a day in the years 2011 or 2012. Model Overview. [SQL Server][R Language]資料科學用戶端(二)使用RxSqlServerData函數建立SQL Server 資料物件. 除了已經在SQL Server資料庫內的資料，有時候也會有其他來源收集的整批資料，這時候就可以在R用戶端呼叫RevoScaleR函數直接在SQL Server內建立資料物件並且匯入資料。